Wireless service providers are observing an exponential growth in mobile communications due to both an increase in consumer demand and commercial requirements. Moreover, to ensure customer satisfaction, wireless service providers aim to deliver a high quality service at any location, to facilitate reliable and efficient mobile communications. However, wireless data usage has become difficult to predict and localize and it is often difficult to tune the wireless network to suit this usage. This is especially evident in venues where large “swarms” of users may congregate and move between locations, which are or are not served well by the macro and/or indoor networks. Indoor networks are complex, expensive, location-specific and static. Moreover, indoor networks are effective only if the users swarm/congregate near the antennas, but have no means to adapt to moving swarms. It is also difficult, costly, and time-consuming to implement indoor networks in every location susceptible to swarms of users. In cases where swarms are driven by infrequent or random events, it is difficult to predict and justify return on investment for indoor networks. Additionally, it is difficult to adequately predict and provision capacity, before the swarm actually occurs.
Conventional systems also utilize global positioning system (GPS) data, received from user equipments (UEs), indicative of UE locations, to detect swarms. However, this approach negatively affects battery life of the UEs. In addition, this approach requires network resources to report UE location data. Moreover, if a UE, in a swarm, cannot acquire radio resources to report the GPS data, the swarm cannot be detected in time by the network. Further, GPS signals are also difficult to acquire indoors, where swarms are most likely to occur. In another example, smart-antennas can be utilized in macro cells to potentially localize and tune themselves to better serve swarms of users. However, smart-antennas are highly complex, expensive and large. In addition, smart-antennas only react to in-progress traffic (e.g., after a UE establishes voice/data communication) and are unable to predict or improve idle-mode (e.g., before a UE establishes voice/data communication) signal strength or perception.